Non-destructive Evaluation and Inspection (“NDE/I”) technologies generally provide ways to nondestructively scan, image, sense or otherwise evaluate characteristics of materials and/or components. In particular, NDE/I technologies may be used to detect minute flaws and defects in those materials and/or component parts. As such, NDE/I technologies have become increasingly used to help assure structural and functional integrity, safety, and cost effective sustainment of various assets, during both initial manufacture and operational service. More specifically, NDE/I technologies have been increasingly used in determining the wear and tear of assets that are pushed to their physical limits, such as military vehicles. As the average age of the various assets increases, particularly beyond the originally contemplated design life, the importance of using NDE/I technologies to detect structural damage before that damage advances to structural failure is paramount.
Non-destructive evaluation (“NDE”) data is often gathered from NDE data collection devices and may include x-ray images of at least a portion of an asset, such as the wing of an aircraft. By analyzing a dataset of NDE data, defects or other structural irregularities of the asset at the location associated with that dataset of NDE data can be detected. However, this NDE data is typically difficult to manage and handle. For example, the NDE data is often large in size, associated with merely a portion of the asset, and also must be matched with a particular location on the asset. To determine wear and tear, structural damage and/or other irregularities of an entire component of an asset may require the analysis of tens (if not hundreds) of individual datasets of NDE data. This results in numerous datasets of NDE data for each asset, and thus even more datasets of NDE data for a fleet of assets. As each dataset of NDE data is often inspected, this results in large amounts of data that are difficult to categorize and otherwise analyze in whole. Moreover, the NDE data may be discarded after it has been analyzed, and thus there is often little NDE data for an asset over time. Thus, when a potential problem is indicated, it is often difficult to track that indication on an asset through time, analyze that indication in relation to other indications of the asset, and analyze that indication in relation to indications of a plurality of similar assets.
As the amount of NDE data increases, so do associated costs and needs for users trained to perform inspections. Although NDE data collection devices may produce digital data, the digital data is being generated without systems in place to manage and archive the collected information. Moreover, the analysis of NDE data is often laborious and crude. Some conventional systems receive NDE data and align it to a simulated model of a portion of an asset through the use of manual tools. However, these manual tools require human interaction and generally require a user experienced with that NDE data and/or asset to align and analyze that NDE data. Although some conventional systems have used automatic alignment of the NDE data, these methods often fail as a method of alignment for one dataset of NDE data is typically not useful for another dataset of NDE data. Thus, conventional systems are typically unable to align datasets of NDE data that are in turn associated multiple modalities (e.g., datasets of NDE data captured with various NDE data collection devices). This often has the effect of tying particular method of alignments to particular NDE data collection devices, and thus increases the cost of NDE data capture and analysis.
Consequently, there is a continuing need to manage datasets of NDE data of an asset or fleet of assets over time, as well as a continuing need to support the alignment of multiple modalities of NDE data in an extensible platform that supports NDE data fusion.